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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | # PS7.5, for the application in Section 7.4
open data7-2
square EDUC EXPER AGE
# The above generates squares. The following generate interactions
genr ED_GEN=EDUC*GENDER
genr ED_RACE=EDUC*RACE
genr ED_CLER=EDUC*CLERICAL
genr ED_MAINT=EDUC*MAINT
genr ED_CRAFT=EDUC*CRAFTS
genr AGE_GEN=AGE*GENDER
genr AGE_RACE=AGE*RACE
genr AGE_CLER=AGE*CLERICAL
genr AGE_MAIN=AGE*MAINT
genr AGE_CRFT=AGE*CRAFTS
genr EXP_GEN=EXPER*GENDER
genr EXP_RACE=EXPER*RACE
genr EXP_CLER=EXPER*CLERICAL
genr EXP_MAIN=EXPER*MAINT
genr EXP_CRFT=EXPER*CRAFTS
genr LWAGE = ln(WAGE)
list
# The following is the basic model
ols LWAGE const EDUC EXPER AGE
genr DFR = $df
genr ut = $uhat
# Auxiliary regression
ols ut const EDUC EXPER AGE GENDER RACE CLERICAL MAINT CRAFTS sq_EDUC \
sq_EXPER sq_AGE ED_GEN ED_RACE ED_CLER ED_MAINT ED_CRAFT AGE_GEN \
AGE_RACE AGE_CLER AGE_MAIN AGE_CRFT EXP_GEN EXP_RACE EXP_CLER EXP_MAIN \
EXP_CRFT
# Coefficients etc. for above are same as those in kitchen sink model
genr DFU = $df
# compute the number of restrictions, LM statistic, and pvalue
genr NR = DFR - DFU
genr LM = $nrsq
pvalue X NR LM
# The following is Model 1 in Table 7.5
ols LWAGE const EDUC EXPER AGE GENDER RACE sq_EDUC sq_EXPER sq_AGE \
ED_GEN ED_CLER ED_MAINT ED_CRAFT AGE_GEN AGE_RACE AGE_MAIN AGE_CRFT \
EXP_RACE EXP_CRFT
# omit variables with insiginficant coefficients, a few at a time
omit RACE AGE
omit GENDER
omit sq_EXPER AGE_GEN
omit EXP_RACE AGE_RACE
# Final model labeled Model 2 in Table 7.5
omit EXP_CRFT
# Model 3 in Table 7.4 -- kitchen sink specification
ols LWAGE const EDUC EXPER AGE GENDER RACE CLERICAL MAINT CRAFTS sq_EDUC \
sq_EXPER sq_AGE ED_GEN ED_RACE ED_CLER ED_MAINT ED_CRAFT AGE_GEN \
AGE_RACE AGE_CLER AGE_MAIN AGE_CRFT EXP_GEN EXP_RACE EXP_CLER EXP_MAIN \
EXP_CRFT
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